The term “neural network” often refers to models employed in statistics, cognitive psychology and artificial intelligence. Neural network models emulate, to some degree, the central nervous system. Neural networks can be characterized by principles of non-linear, distributed, parallel and local processing and adaptation. Neural networks can be implemented in software and digital logic. Modern software implementations of artificial neural networks are based on statistics and signal processing. In some of these systems, neural networks or parts of neural networks (like artificial neurons) form components in larger systems that combine both adaptive and non-adaptive elements.
A neural network can be described as a type of statistical model that consists of a set of inputs that can produce a set of outputs using sets of adaptive weights. The weights affect how the inputs are combined to produce logical outputs. A classic model of a neural network has a set of input nodes, a set of output nodes, and a set in intermediate or hidden nodes. Sets of inputs are connected to sets of intermediate nodes, and intermediate nodes are connected to sets of output nodes. Adaptive weights are used to determine how the value of each input node influences the output of the intermediate node. The combination of values from the intermediate nodes at each output influences that output. Many variants of this type of model have been implemented in circuitry and software.
Implementing neural networks in digital circuits can have space consumption issues due to the need for granularity of the weighting values and the binary nature of digital circuits. Since the advent of electronic circuits, the miniaturization of transistors and logic devices has been a universal goal to advance the capabilities and applicability of electronic devices. Neural networks implemented in circuitry are no exception.
There is a need therefore, for an implementation of neural networks that increasingly miniaturizes circuit implementation of at least some forms of neural networks.